Object detection and classification from large‐scale cluttered indoor scans

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How researchers see this paper

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ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, background): “A large number of RGB-D datasets have been captured and made publicly available for training and benchmarking [56, 34, 50, 65, 79, 83, 74, 4, 58, 81, 15, 55, 1, 68, 30, 51, 21, 48, 43, 92, 80, 61, 72, 93, 36, 16, 35, 57, 40, 29, 70, 52, 45, 95, 75, 9, 33, 85, 71, 32, 3, 10, 78, 2].”
SUN RGB-D: A RGB-D scene understanding benchmark suite (2015, unknown): “There are many existing RGB-D datasets available [54] [47] [1] [25] [44] [60] [49] [45] [29] [66] [57] [52] [46] [16] [21] [73] [35] [67] [3] [41] [10] [63] [42] [64] [65] [48] [12] [33] [8] [50] [26] [6].”
3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017, unknown): “[35] uses plane fitting to complete small missing regions, while [32] [34] [40] [48] [52] [56] applies shape symmetry to fill in holes.”
RAPter (2015, methodology): “2012], office environments [Kim et al. 2012; Mattausch et al. 2014], and also for outdoor buildings [Zheng et al.”
3D all the way: Semantic segmentation of urban scenes from start to end in 3D (2015, result): “This results in competitive results with signiﬁcant speed beneﬁts.”